Protein arrays and microarrays

In the past, studies of protein activities have focused on studying a single protein at a time, which is often time-consuming and expensive. Recently, with the sequencing of entire genomes, large-scale proteome analysis has begun. Arrays of proteins have been used for the determination of subcellular localization, analysis of protein-protein interactions and biochemical analysis of protein function. New protein-microarray technologies have been introduced that enable the high-throughput analysis of protein activities. These have the potential to revolutionize the analysis of entire proteomes.     

Protein microarrays as tools for functional proteomics

Protein microarrays present an innovative and versatile approach to study protein abundance and function at an unprecedented scale. Given the chemical and structural complexity of the proteome, the development of protein microarrays has been challenging. Despite these challenges there has been a marked increase in the use of protein microarrays to map interactions of proteins with various other molecules, and to identify potential disease biomarkers, especially in the area of cancer biology. In this review, we discuss some of the promising advances made in the development and use of protein microarrays.     

Protein microarrays: promising tools for proteomic research

Miniaturized and parallelized ligand binding assays are of great interest in postgenomic research because microarray technology allows the simultaneous determination of a large number of parameters from a minute amount of sample within a single experiment. Assay systems based on this technology are used for the identification and quantification of proteins as well as for the study of protein interactions. Protein affinity assays have been implemented that allow the analysis of interactions between proteins with other proteins, peptides, low molecular weight compounds, oligosaccharides or DNA. Microarray technology is an emerging technology used in global analytical approaches and has a considerable impact on proteomic research.     

Protein microarrays and their applications

In recent years, the importance of proteomic works, such as protein expression, detection and identification, has grown in the fields of proteomic and diagnostic research. This is because complete genome sequences of humans, and other organisms, progress as cellular processing and controlling are performed by proteins as well as DNA or RNA. However, conventional protein analyses are time-consuming; therefore, high throughput protein analysis methods, which allow fast, direct and quantitative detection, are needed. These are so-called protein microarrays or protein chips, which have been developed to fulfill the need for high-throughput protein analyses. Although protein arrays are still in their infancy, technical development in immobilizing proteins in their native conformation on arrays, and the development of more sensitive detection methods, will facilitate the rapid deployment of protein arrays as high-throughput protein assay tools in proteomics and diagnostics. This review summarizes the basic technologies that are needed in the fabrication of protein arrays and their recent applications.     

Standards for systems biology

High-throughput technologies are generating large amounts of complex data that have to be stored in databases, communicated to various data analysis tools and interpreted by scientists. Data representation and communication standards are needed to implement these steps efficiently. Here we give a classification of various standards related to systems biology and discuss various aspects of standardization in life sciences in general. Why are some standards more successful than others, what are the prerequisites for a standard to succeed and what are the possible pitfalls?     

Protein microarrays for gene expression and antibody screening.

Proteins translate genomic sequence information into function, enabling biological processes. As a complementary approach to gene expression profiling on cDNA microarrays, we have developed a technique for high-throughput gene expression and antibody screening on chip-size protein microarrays. Using a picking/spotting robot equipped with a new transfer stamp, protein solutions were gridded onto polyvinylidene difluoride filters at high density. Specific purified protein was detected on the filters with high sensitivity (250 amol or 10 pg of a test protein). On a microarray made from bacterial lysates of 92 human cDNA clones expressed in a microtiter plate, putative protein expressors could be reliably identified. The rate of false-positive clones, expressing proteins in incorrect reading frames, was low. Product specificity of selected clones was confirmed on identical microarrays using monoclonal antibodies. Cross-reactivities of some antibodies with unrelated proteins imply the use of protein microarrays for antibody specificity screening against whole libraries of proteins. Because this application would not be restricted to antigen-antibody systems, protein microarrays should provide a general resource for high-throughput screens of gene expression and receptor-ligand interactions.     

Protein microarrays for multiplex analysis of signal transduction pathways

We have developed a multiplexed reverse phase protein (RPP) microarray platform for simultaneous monitoring of site-specific phosphorylation of numerous signaling proteins using nanogram amounts of lysates derived from stimulated living cells. We first show the application of RPP microarrays to the study of signaling kinetics and pathway delineation in Jurkat T lymphocytes. RPP microarrays were used to profile the phosphorylation state of 62 signaling components in Jurkat T cells stimulated through their membrane CD3 and CD28 receptors, identifying a previously unrecognized link between CD3 crosslinking and dephosphorylation of Raf-1 at Ser259. Finally, the potential of this technology to analyze rare primary cell populations is shown in a study of differential STAT protein phosphorylation in interleukin (IL)-2-stimulated CD4(+)CD25(+) regulatory T cells. RPP microarrays, prepared using simple procedures and standard microarray equipment, represent a powerful new tool for the study of signal transduction in both health and disease.     

Protein microarrays and novel detection platforms

The field of proteomics has undergone rapid advancements over the last decade and protein microarrays have emerged as a promising technological platform for the challenging task of studying complex proteomes. This gel-free approach has found an increasing number of applications due to its ability to rapidly and efficiently study thousands of proteins simultaneously. Different protein microarrays, including capture arrays, reverse-phase arrays, tissue microarrays, lectin microarrays and cell-free expression microarrays, have emerged, which have demonstrated numerous applications for proteomics studies including biomarker discovery, protein interaction studies, enzyme-substrate profiling, immunological profiling and vaccine development, among many others. The need to detect extremely low-abundance proteins in complex mixtures has provided motivation for the development of sensitive, real-time and multiplexed detection platforms. Conventional label-based approaches like fluorescence, chemiluminescence and use of radioactive isotopes have witnessed substantial advancements, with techniques like quantum dots, gold nanoparticles, dye-doped nanoparticles and several bead-based methods now being employed for protein microarray studies. In order to overcome the limitations posed by label-based technologies, several label-free approaches like surface plasmon resonance, carbon nanotubes and nanowires, and microcantilevers, among others, have also advanced in recent years, and these methods detect the query molecule itself. The scope of this article is to outline the protein microarray techniques that are currently being used for analytical and function-based proteomics and to provide a detailed analysis of the key technological advances and applications of various detection systems that are commonly used with microarrays.     

Protein microarrays to study carbohydrate-recognition events

In order to expand areas in which protein microarrays can be used to solve important biological problems, we have investigated ways in which the technique can be employed for functional glycomics. Initially, our protein microarrays were used for the rapid identification of carbohydrate-binding proteins using trifunctional carbohydrate probes and fluorescent dye-labeled polysaccharides. Glycan probes were selectively bound to the corresponding lectins immobilized on the solid surface. In addition, these microarrays were also employed for profiling of carbohydrates on Jurkat T-cell surfaces. These cells adhered to ConA, RCA(120), SNA and WGA, indicating expression of alpha-Man, Gal, NeuNAcalpha2,6Gal and GlcNAc residues on their surfaces. Furthermore, we determined binding affinities between WGA and carbohydrates by measuring IC(50) values of GlcNAc that inhibited 50% of trivalent GlcNAc binding to WGA immobilized on the solid surface. All the experiments show that protein microarrays can be used to study carbohydrate-recognition events in the field of glycomics.     

Interaction networks for systems biology

Cellular functions are almost always the result of the coordinated action of several proteins, interacting in protein complexes, pathways or networks. Progress made in devising suitable tools for analysis of protein-protein interactions, have recently made it possible to chart interaction networks on a large-scale. The aim of this review is to provide a short overview of the most promising contributions of interaction networks to human biology, structural biology and human genetics.     

Imaging methodologies for systems biology

Systems biology has recently achieved significant success in the understanding of complex interconnected phenomena such as cell polarity and migration. In this context, the definition of systems biology has come to encompass the integration of quantitative measurements with sophisticated modeling approaches. This article will review recent progress in live cell imaging technologies that have expanded the possibilities of quantitative in vivo measurements, particularly in regards to molecule counting and quantitative measurements of protein concentration and dynamics. These methods have gained and continue to gain popularity with the biological community. In general, we will discuss three broad categories: protein interactions, protein quantitation, and protein dynamics.     

Pathway information for systems biology

Pathway information is vital for successful quantitative modeling of biological systems. The almost 170 online pathway databases vary widely in coverage and representation of biological processes, making their use extremely difficult. Future pathway information systems for querying, visualization and analysis must support standard exchange formats to successfully integrate data on a large scale. Such integrated systems will greatly facilitate the constructive cycle of computational model building and experimental verification that lies at the heart of systems biology.     

Mining literature for systems biology

Currently, literature is integrated in systems biology studies in three ways. Hand-curated pathways have been sufficient for assembling models in numerous studies. Second, literature is frequently accessed in a derived form, such as the concepts represented by the Medical Subject Headings (MeSH) and Gene Ontologies (GO), or functional relationships captured in protein-protein interaction (PPI) databases; both of these are convenient, consistent reductions of more complex concepts expressed as free text in the literature. Moreover, their contents are easily integrated into computational processes required for dealing with large data sets. Last, mining text directly for specific types of information is on the rise as text analytics methods become more accurate and accessible. These uses of literature, specifically manual curation, derived concepts captured in ontologies and databases, and indirect and direct application of text mining, will be discussed as they pertain to systems biology.     

Advanced computing for systems biology

Systems biology is based on computational modelling and simulation of large networks of interacting components. Models may be intended to capture processes, mechanisms, components and interactions at different levels of fidelity. Input data are often large and geographically disperse, and may require the computation to be moved to the data, not vice versa. In addition, complex system-level problems require collaboration across institutions and disciplines. Grid computing can offer robust, scaleable solutions for distributed data, compute and expertise. We illustrate some of the range of computational and data requirements in systems biology with three case studies: one requiring large computation but small data (orthologue mapping in comparative genomics), a second involving complex terabyte data (the Visible Cell project) and a third that is both computationally and data-intensive (simulations at multiple temporal and spatial scales). Authentication, authorisation and audit systems are currently not well scalable and may present bottlenecks for distributed collaboration particularly where outcomes may be commercialised. Challenges remain in providing lightweight standards to facilitate the penetration of robust, scalable grid-type computing into diverse user communities to meet the evolving demands of systems biology.